• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.1-20
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• 2022

As the global business environment is rapidly changing due to the 4th industrial revolution, new jobs that did not exist before are emerging. Among them, the job that companies are most interested in is 'Data Scientist'. As information and communication technologies take up most of our lives, data on not only online activities but also offline activities are stored in computers every hour to generate big data. Companies put a lot of effort into discovering new opportunities from such big data. The new job that emerged along with the efforts of these companies is data scientist. The demand for data scientist, a promising job that leads the big data era, is constantly increasing, but its supply is not still enough. Although data analysis technologies and tools that anyone can easily use are introduced, companies still have great difficulty in finding proper experts. One of the main reasons that makes the data scientist's shortage problem serious is the lack of understanding of the data scientist's job. Therefore, in this study, we explore the job competencies of a data scientist by qualitatively analyzing the actual job posting information of the company. This study finds that data scientists need not only the technical and system skills required of software engineers and system analysts in the past, but also business-related and interpersonal skills required of business consultants and project managers. The results of this study are expected to provide basic guidelines to people who are interested in the data scientist profession and to companies that want to hire data scientists.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.86-95
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• 2022

Online recruitment websites discuss job demands in various fields, and job postings contain detailed job specifications. Analyzing this text can elucidate the features that determine job salaries. Text embedding models can learn the contextual information in a text, and explainable artificial intelligence frameworks can be used to examine in detail how text features contribute to the models' outputs. We collected 733,625 job postings using the WORKNET API and classified them into low, mid, and high-range salary groups. A text embedding model that predicts job salaries based on the text in job postings was trained with the collected data. Then, we applied the SHapley Additive exPlanations (SHAP) framework to the trained model and discovered the significant words that determine each salary class. Several limitations and remaining words are also discussed.

• Journal of Korean Academy of Dental Administration
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• v.6 no.1
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• pp.43-47
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• 2018

Online data can be explored for topics browsed by an unspecified population to detect professional information demands more quickly. The purpose of this study was to collect and analyze online questionnaires in order to find information required by dental hygienists. We analyzed the frequency of posting words after isolating nouns from questions of the Korean Dental Hygienists Association homepage's Q & A section, the Naver Knowledge-iN service, and a dental hygienists' online meeting site in Naver. We found that queries of the Korean Dental Hygienists Association's homepage were concentrated on education renewal and license notification. The queries about dental hygienists in the Naver Knowledge-iN service used words related to job or career choice, and the queries of the dental hygienist-affiliated site had many words related to dental practice, dental work, and turnover. This study showed that the information needs of unspecified dental hygienists varied depending on the online environment such as homepage, blog, and information service.

• Journal of Information Science Theory and Practice
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• v.5 no.1
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• pp.47-64
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• 2017

Purpose: This study aims to assess the level of job satisfaction among the practicing Library and Information Science (LIS) professionals in India. Some of the key variables studied include salary, work environment, professional position, working hours, location, employee participation in administrative matters, recognition, and advancement. Scope: The study is limited to LIS professionals in India; however, the results of the study show some degree of commonality among LIS professionals across the world. Methodology/Approach: The study has been purely undertaken by analyzing the primary data collected from practicing library and information professionals across India by circulating an online questionnaire, using Google services and by posting the link on various social groups and other social networking sites such as Groups, Facebook, LinkedIn, discussion forums, and e-mails. Findings: Responses were generated across 20 states and union territories of the country, representing 88.15% territory of India. Of the total responses generated, 72.88% are males and 27.12% females. From the data analysis it emerged that every third library professional in India enters in to LIS profession by chance and not by choice, as 35.59% respondents expressed that they landed in the LIS profession by chance and 60.45% by choice. More than 77% respondents have expressed their willingness to change their present employer, if given better opportunity. More than 80% professionals either fully or partly agree that most of the LIS professionals across India are dissatisfied with their employers. All these and many more areas have been explored, which reflect the aspects of job satisfaction and dissatisfaction among LIS professionals in India.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• Information Systems Review
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• v.25 no.3
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• pp.179-197
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• 2023

As a sufficient workforce supports the industry's growth, workforce training has also been carried out as part of the industry promotion policy. However, the market still has a shortage of skilled mid-level workers. The information security disclosure requires organizations to secure personnel responsible for information security work. Still, the division between information technology work and job areas is unclear, and the pay is not high for responsibility. This paper compares job keywords in advertisements for the information security workforce for 2014, 2019, and 2022. There is no difference in the keywords describing the job duties of information security personnel in the three years, such as implementation, operation, technical support, network, and security solution. To identify the actual needs of companies, we also analyzed and compared the contents of job advertisements posted on online recruitment sites with information security sector knowledge and skills defined by the National Competence Standards used for comprehensive vocational training. It was found that technical skills such as technology development, network, and operating system are preferred in the actual workplace. In contrast, managerial skills such as the legal system and certification systems are prioritized in vocational training.